Metareasoning for Safe Decision Making in Autonomous Systems

Svegliato, Justin; Basich, Connor; Saisubramanian, Sandhya; Zilberstein, Shlomo

doi:10.1109/icra46639.2022.9811887

Cited by 5 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to recent work on metareasoning (Svegliato et al 2019(Svegliato et al , 2022, the metareasoning architecture in Figure 2 has a meta-level process that monitors and controls an object-…”

Section: Anytime Weighted A* Examplementioning

confidence: 99%

Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep Reinforcement Learning

Bhatia

Svegliato

Nashed

et al. 2022

ICAPS

Self Cite

View full text Add to dashboard Cite

Anytime planning algorithms often have hyperparameters that can be tuned at runtime to optimize their performance. While work on metareasoning has focused on when to interrupt an anytime planner and act on the current plan, the scope of metareasoning can be expanded to tuning the hyperparameters of the anytime planner at runtime. This paper introduces a general, decision-theoretic metareasoning approach that optimizes both the stopping point and hyperparameters of anytime planning. We begin by proposing a generalization of the standard meta-level control problem for anytime algorithms. We then offer a meta-level control technique that monitors and controls an anytime algorithm using deep reinforcement learning. Finally, we show that our approach boosts performance on a common benchmark domain that uses anytime weighted A* to solve a range of heuristic search problems and a mobile robot application that uses RRT* to solve motion planning problems.

show abstract

Section: Anytime Weighted A* Examplementioning

confidence: 99%

Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep Reinforcement Learning

Bhatia

Svegliato

Nashed

et al. 2022

ICAPS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Metareasoning as an anytime algorithm improves the agent's decision-making process based on the current situation [14], [15], [16], [17], [18], [19], [20]. As a result, the drone which is the agent can dynamically switch between cloud-based and edge computing implementation while considering the power consumption, latency constraints, and model accuracy metric.…”

Section: Introduction and Related Workmentioning

confidence: 99%

MLAE2: Metareasoning for Latency-Aware Energy-Efficient Autonomous Nano-Drones

Navardi

2023

2023 IEEE International Symposium on Circuits and Systems (ISCAS)

View full text Add to dashboard Cite

Safety, low-cost, small size, and Artificial Intelligence (AI) capabilities of drones have led to the proliferation of autonomous tiny Unmanned Aerial Vehicles (UAVs) in many applications which are dangerous, unknown, or time-consuming for humans. Deep Neural Networks (DNNs) have enabled autonomous navigation while using captured data by drone sensors as input to the model. Due to the extreme complexity of DNNs, cloud-based approaches have been highly addressed in which a drone is connected to the cloud and sends the data to the cloud, and takes the result. On the other hand, emerging tiny machine learning models and edge computing brings significant improvement in energy efficiency and latency with respect to cloud-based approaches. However, there is a trade-off in these two implementations for model accuracy, latency, and energy efficiency. For instance, applying tiny machine learning models leads to lower latency but it sacrifices model accuracy in comparison to cloud-based computing. To address these challenges, we consider multiple models and introduce a new approach named MLAE2 which applies Metareasoning approach for Latency-Aware Energy-Efficient autonomous drones. Metareasoning monitors parameters such as latency and energy consumption for different algorithms and chooses the appropriate algorithm due to the environmental situation changes. To Evaluate our approach we extract the power consumption and latency for both cloudbased computing and edge computing while deploying multiple models on a tiny drone named Crazyflie. The experimental results show that MLAE2 successfully meets the latency constraint while maximizing model accuracy and improving energy efficiency.

show abstract

Metareasoning Design Options

Herrmann

2023

Synthesis Lectures on Computer Science

View full text Add to dashboard Cite

Metareasoning for Safe Decision Making in Autonomous Systems

Cited by 5 publications

References 13 publications

Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep Reinforcement Learning

Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep Reinforcement Learning

MLAE2: Metareasoning for Latency-Aware Energy-Efficient Autonomous Nano-Drones

Metareasoning Design Options

Contact Info

Product

Resources

About